Only Aggressive Elephants are Fast Elephants

Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider's orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.Comment: VLDB201

Vectorwise: Beyond Column Stores

textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution of its technical architecture, customer reactions to the product and its future research and development roadmap. One take-away from this story is that the novelty in Vectorwise is much more than just column-storage: it boasts many query processing innovations in its vectorized execution model, and an adaptive mixed row/column data storage model with indexing support tailored to analytical workloads. Another one is that there is a long road from research prototype to commercial product, though database research continues to achieve a strong innovative influence on product development

An Algorithm for Data Reorganization in a Multi-dimensional Index

In spatial databases, data are associated with spatial coordinates and are retrieved based on spatial proximity. A spatial database uses spatial indexes to optimize spatial queries. An essential ingredient for efficient spatial query processing is spatial clustering of data and reorganization of spatial data. Traditional clustering algorithms and reorganization utilities lack in performance and execution. To solve this problem we have developed an algorithm to convert a two dimensional spatial index into a single dimensional value and then a reorganization is done on the spatial data. This report describes this algorithm as well as various experiments to validate its effectiveness

On the analysis of big data indexing execution strategies

Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of non-clustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained

Query Optimization Techniques for OLAP Applications: An ORACLE versus MS-SQL Server Comparative Study

Query optimization in OLAP applications is a novel problem. A lot of research was introduced in the area of optimizing query performance, however great deal of research focused on OLTP applications rather than OLAP. In order to reach the output results OLAP queries extensively asks the database, inefficient processing of those queries will have its negative impact on the performance and may make the results useless. Techniques for optimizing queries include memory caching, indexing, hardware solutions, and physical database storage. Oracle and MS SQL Server both offer OLAP optimization techniques, the paper will review both packages’ approaches and then proposes a query optimization strategy for OLAP applications. The proposed strategy is based on use of the following four ingredients: 1- intermediate queries; 2- indexes both BTrees and Bitmaps; 3- memory cache (for the syntax of the query) and secondary storage cache (for the result data set); and 4- the physical database storage (i.e. binary storage model) accompanied by its hardware solution

Learning Multi-dimensional Indexes

Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes such as R-trees, or use complex sort orders (e.g., Z-ordering). However, these schemes are often hard to tune and their performance is inconsistent across different datasets and queries. In this paper, we introduce Flood, a multi-dimensional in-memory index that automatically adapts itself to a particular dataset and workload by jointly optimizing the index structure and data storage. Flood achieves up to three orders of magnitude faster performance for range scans with predicates than state-of-the-art multi-dimensional indexes or sort orders on real-world datasets and workloads. Our work serves as a building block towards an end-to-end learned database system